{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. 生成模拟数据集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Fx8Ly7VVB2kaGgHeW7+b20wd2yllwejSe+3Yb//l2G0JAVpKdV2+eQkqcrcW2dqtKfprK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e65f320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "dataset_size = 200\n",
    "data = []\n",
    "label = []\n",
    "np.random.seed(0)\n",
    "\n",
    "# 以原点为圆心，半径为1的圆把散点划分成红蓝两部分，并加入随机噪音。\n",
    "for i in range(dataset_size):\n",
    "    x1 = np.random.uniform(-1,1)\n",
    "    x2 = np.random.uniform(0,2)\n",
    "    if x1**2 + x2**2 <= 1:\n",
    "        data.append([np.random.normal(x1, 0.1),np.random.normal(x2,0.1)])\n",
    "        label.append(0)\n",
    "    else:\n",
    "        data.append([np.random.normal(x1, 0.1), np.random.normal(x2, 0.1)])\n",
    "        label.append(1)\n",
    "        \n",
    "data = np.hstack(data).reshape(-1,2)\n",
    "label = np.hstack(label).reshape(-1, 1)\n",
    "plt.scatter(data[:,0], data[:,1], c=label,\n",
    "           cmap=\"RdBu\", vmin=-.2, vmax=1.2, edgecolor=\"white\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. 定义一个获取权重，并自动加入正则项到损失的函数。一般tf.Variable都是需要训练的权重参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_weight(shape, var_lambda):\n",
    "    w = tf.Variable(tf.random_normal(shape), dtype=tf.float32)\n",
    "    tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(var_lambda)(w))\n",
    "    return w"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. 定义神经网络。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = tf.placeholder(tf.float32, shape=(None, 2))\n",
    "y_ = tf.placeholder(tf.float32, shape=(None, 1))\n",
    "\n",
    "# 每层节点的个数\n",
    "layer_dimension = [2,10,5,3,1]\n",
    "\n",
    "n_layers = len(layer_dimension)\n",
    "\n",
    "cur_layer = x\n",
    "in_dimension = layer_dimension[0]\n",
    "\n",
    "# 循环生成网络结构，输入层X[None, 2]，隐藏层W1[2, 10]、W2[10, 5]、W3[5, 3]，输出层Y[None, 1]\n",
    "for i in range(1, n_layers):\n",
    "    out_dimension = layer_dimension[i]\n",
    "    weight = get_weight([in_dimension, out_dimension], 0.003)\n",
    "    bias = tf.Variable(tf.constant(0.1, shape=[out_dimension]))\n",
    "    cur_layer = tf.nn.elu(tf.matmul(cur_layer, weight) + bias)\n",
    "    in_dimension = layer_dimension[i]\n",
    "\n",
    "y= cur_layer\n",
    "\n",
    "# 损失函数的定义。\n",
    "mse_loss = tf.reduce_sum(tf.pow(y_ - y, 2)) / dataset_size\n",
    "tf.add_to_collection('losses', mse_loss)\n",
    "loss = tf.add_n(tf.get_collection('losses'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. 训练不带正则项的损失函数mse_loss。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After 999 steps, mse_loss: 0.057901\n",
      "After 1999 steps, mse_loss: 0.046656\n",
      "After 2999 steps, mse_loss: 0.040352\n",
      "After 3999 steps, mse_loss: 0.033711\n",
      "After 4999 steps, mse_loss: 0.027549\n",
      "After 5999 steps, mse_loss: 0.022838\n",
      "After 6999 steps, mse_loss: 0.018099\n",
      "After 7999 steps, mse_loss: 0.016241\n",
      "After 8999 steps, mse_loss: 0.015107\n",
      "After 9999 steps, mse_loss: 0.014370\n"
     ]
    },
    {
     "data": {
      "image/png": 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7+WfhFlkkuZeDw5UnUiNVTUfXDea0QGvGbpGYOzyJmYMSsMnSWdsf9iTO+UoI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\nnDiRBg0aMHDgQH7++eca2RKzRht3hKDx2Id9Bt7cIJmkESOY9tsRsq0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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1138af1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 定义训练的目标函数mse_loss，训练次数及训练模型\n",
    "train_op = tf.train.AdamOptimizer(0.001).minimize(mse_loss)\n",
    "TRAINING_STEPS = 10000\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    tf.global_variables_initializer().run()\n",
    "    for i in range(TRAINING_STEPS):\n",
    "        sess.run(train_op, feed_dict={x: data, y_: label})\n",
    "        if i % 1000 == 1000 - 1:\n",
    "            print(\"After %d steps, mse_loss: %f\" % (i,sess.run(mse_loss, feed_dict={x: data, y_: label})))\n",
    "\n",
    "    # 画出训练后的分割曲线       \n",
    "    xx, yy = np.mgrid[-1.2:1.2:.01, -0.2:2.2:.01]\n",
    "    grid = np.c_[xx.ravel(), yy.ravel()]\n",
    "    probs = sess.run(y, feed_dict={x:grid})\n",
    "    probs = probs.reshape(xx.shape)\n",
    "\n",
    "plt.scatter(data[:,0], data[:,1], c=label,\n",
    "           cmap=\"RdBu\", vmin=-.2, vmax=1.2, edgecolor=\"white\")\n",
    "plt.contour(xx, yy, probs, levels=[.5], cmap=\"Greys\", vmin=0, vmax=.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. 训练带正则项的损失函数loss。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After 999 steps, loss: 0.169548\n",
      "After 1999 steps, loss: 0.139344\n",
      "After 2999 steps, loss: 0.112356\n",
      "After 3999 steps, loss: 0.092295\n",
      "After 4999 steps, loss: 0.077377\n",
      "After 5999 steps, loss: 0.068788\n",
      "After 6999 steps, loss: 0.064754\n",
      "After 7999 steps, loss: 0.063428\n",
      "After 8999 steps, loss: 0.062977\n",
      "After 9999 steps, loss: 0.062835\n"
     ]
    },
    {
     "data": {
      "image/png": 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4WpUvOBFW7qgJ0jbSDXh75R7unDmoU5MFl1flhW+2859vtmMYkJFg59VbJpEUY2uxrd0q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\nf0ug6rOywcsj87fwm4uKO7V/wzAoLy9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/9isDN/7Om+l7aRYdw6xLr+CHNWtouz8T/fhx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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e879668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 定义训练的目标函数loss，训练次数及训练模型\n",
    "train_op = tf.train.AdamOptimizer(0.001).minimize(loss)\n",
    "TRAINING_STEPS = 10000\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    tf.global_variables_initializer().run()\n",
    "    for i in range(TRAINING_STEPS):\n",
    "        sess.run(train_op, feed_dict={x: data, y_: label})\n",
    "        if i % 1000 == 1000 - 1:\n",
    "            print(\"After %d steps, loss: %f\" % (i, sess.run(loss, feed_dict={x: data, y_: label})))\n",
    "\n",
    "    # 画出训练后的分割曲线       \n",
    "    xx, yy = np.mgrid[-1:1:.01, 0:2:.01]\n",
    "    grid = np.c_[xx.ravel(), yy.ravel()]\n",
    "    probs = sess.run(y, feed_dict={x:grid})\n",
    "    probs = probs.reshape(xx.shape)\n",
    "\n",
    "plt.scatter(data[:,0], data[:,1], c=label,\n",
    "           cmap=\"RdBu\", vmin=-.2, vmax=1.2, edgecolor=\"white\")\n",
    "plt.contour(xx, yy, probs, levels=[.5], cmap=\"Greys\", vmin=0, vmax=.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
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    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "window_display": false
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 },
 "nbformat": 4,
 "nbformat_minor": 1
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